Aerosol-layer height is essential for understanding the impact of aerosols on the climate system. As part of the European Space Agency Aerosol_cci project, aerosol-layer height as derived from passive thermal and solar satellite sensors measurements have been compared with aerosol-layer heights estimated from CALIOP measurements. The Aerosol_cci project targeted dust-type aerosol for this study. This ensures relatively unambiguous aerosol identification by the CALIOP processing chain. Dust-layer height was estimated from thermal IASI measurements using four different algorithms (from BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and SCIAMACHY (IUP) measurements. Due to differences in overpass time of the various satellites, a trajectory model was used to move the CALIOP-derived dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height pixels. It is not possible to construct a unique dust-layer height from the CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative extinction height defined as the height where the CALIOP extinction column is half of the total extinction column, and the geometric mean height, which is defined as the geometrical mean of the top and bottom heights of the dust layer. In statistical average over all IASI data there is a general tendency to a positive bias of 0.5–0.8 km against CALIOP extinction-weighted height for three of the four algorithms assessed, while the fourth algorithm has almost no bias. When comparing geometric mean height there is a shift of −0.5 km for all algorithms (getting close to zero for the three algorithms and turning negative for the fourth). The standard deviation of all algorithms is quite similar and ranges between 1.0 and 1.3 km. When looking at different conditions (day, night, land, ocean), there is more detail in variabilities (e.g. all algorithms overestimate more at night than during the day). For the solar sensors it is found that on average SCIAMACHY data are lower by −1.097 km (−0.961 km) compared to the CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are lower by −1.393 km (−0.818 km).

Clouds remain one of the largest sources of uncertainty in remote sensing of surface temperature in the infrared, but this uncertainty has not generally been quantified. We present a new approach to do so, applied here to the Advanced Along-Track Scanning Radiometer (AATSR). We use an ensemble of cloud masks based on independent methodologies to investigate the magnitude of cloud detection uncertainties in area-average Land Surface Temperature (LST) retrieval. We find that at a grid resolution of 625 km 2 (commensurate with a 0.25 ∘ grid size at the tropics), cloud detection uncertainties are positively correlated with cloud-cover fraction in the cell and are larger during the day than at night. Daytime cloud detection uncertainties range between 2.5 K for clear-sky fractions of 10–20% and 1.03 K for clear-sky fractions of 90–100%. Corresponding night-time uncertainties are 1.6 K and 0.38 K, respectively. Cloud detection uncertainty shows a weaker positive correlation with the number of biomes present within a grid cell, used as a measure of heterogeneity in the background against which the cloud detection must operate (e.g., surface temperature, emissivity and reflectance). Uncertainty due to cloud detection errors is strongly dependent on the dominant land cover classification. We find cloud detection uncertainties of a magnitude of 1.95 K over permanent snow and ice, 1.2 K over open forest, 0.9–1 K over bare soils and 0.09 K over mosaic cropland, for a standardised clear-sky fraction of 74.2%. As the uncertainties arising from cloud detection errors are of a significant magnitude for many surface types and spatially heterogeneous where land classification varies rapidly, LST data producers are encouraged to quantify cloud-related uncertainties in gridded products.

Mineral dust and ice cloud observations from the Infrared Atmospheric Sounding Interferometer (IASI) are used to assess the relationships between desert dust aerosols and ice clouds over the tropical Atlantic Ocean during the hurricane season 2008. Cloud property histograms are first adjusted for varying cloud top temperature or ice water path distributions with a Bayesian approach to account for meteorological constraints on the cloud variables. Then, histogram differences between dust load classes are used to describe the impact of dust load on cloud property statistics. The analysis of the histogram differences shows that ice crystal sizes are reduced with increasing aerosol load and ice cloud optical depth and ice water path are increased. The distributions of all three variables broaden and get less skewed in dusty environments. For ice crystal size the significant bimodality is reduced and the order of peaks is reversed. Moreover, it is shown that not only are distributions of ice cloud variables simply shifted linearly but also variance, skewness, and complexity of the cloud variable distributions are significantly affected. This implies that the whole cloud variable distributions have to be considered for indirect aerosol effects in any application for climate modelling.

The new Heliosat-4 method estimates the downwelling shortwave irradiance received at ground level in all sky conditions. It provides the global irradiance and its direct and diffuse components on a horizontal plane and the direct irradiance for a plane normal to sun rays. It is a fully physical model using a fast, but still accurate approximation of radiative transfer modelling and is therefore well suited for geostationary satellite retrievals. It can also be used as a fast radiative transfer model in numerical weather prediction models. It is composed of two models based on abaci, also called look-up tables: the already-published McClear model calculating the irradiance under cloud-free conditions and the new McCloud model calculating the extinction of irradiance due to clouds. Both have been realized by using the libRadtran radiative transfer model. The main inputs to Heliosat-4 are aerosol properties, total column water vapour and ozone content as provided by the Copernicus Atmosphere Monitoring Service (CAMS) every 3 h. Cloud properties are derived from images of the Meteosat Second Generation (MSG) satellites in their 15min temporal resolution using an adapted APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) scheme. The 15min means of irradiance estimated by Heliosat-4 are compared to corresponding measurements made at 13 stations within the Baseline Surface Radiation Network and being located in the field of view ofMSG and in various climates. The bias for global irradiance is comprised between 2 and 32Wm-2. The root mean square error (RMSE) ranges between 74 and 94Wm(-2). Relative RMSE values range between 15% and 20% of the mean observed irradiance for stations in desert and Mediterranean climates, and between 26% and 43% for rainy climates with mild winters. Correlation coefficients between 0.91 and 0.97 are found. The bias for the direct irradiance at normal incidence is comprised between -163 and + 50Wm(-2). The RMSE ranges from 160Wm(-2) (29% of the mean observed irradiance) to 288Wm(-2) (63%). The correlation coefficient ranges between 0.67 and 0.87.

Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e., dust aerosol, aerosol absorption).

Volcanic ash is emitted by most eruptions, sometimes reaching the stratosphere. In addition to its climate effect, ash may have a significant impact on civilian flights. Currently, the horizontal distribution of ash aerosols is quite extensively studied, but not its vertical profile, while of high importance for both applications mentioned. Here, we study the sensitivity of the thermal infrared spectral range to the altitude distribution of volcanic ash, based on similar work that was undertaken on mineral dust. We use measurements by the Infrared Atmospheric Sounding Interferometer (IASI) instruments onboard the MetOp satellite series. The retrieval method that we develop for the ash vertical profile is based on the optimal estimation formalism. This method is applied to study the eruption of the Chilean volcano Puyehue, which started on the 4th of June 2011. The retrieved profiles agree reasonably well with Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) measurements, and our results generally agree with literature studies of the same eruption. The retrieval strategy presented here therefore is very promising for improving our knowledge of the vertical distribution of volcanic ash and obtaining a global 3D ash distribution twice a day. Future improvements of our retrieval strategy are also discussed.

In order to re-process DLRs 1km AVHRR data archive to different geophysical and descriptive parameters of the land surface and the atmosphere, a series of scientific data processors are being developed in the framework of the TIMELINE project. The archive of DLR ranges back to the 80ies. The data is preprocessed to L1b and harmonized for sensor effects. The detection of clouds and the processing of cloud properties are done using APOLLO_NG (APOLLO_NextGeneration), which is a probabilistic interpretation of the classical APOLLO scheme, building on its physical principles. Cloud detection is expressed as cloud probability for each satellite pixel. Furthermore, the probabilistic approach of APOLLO_NG allows the retrieval of cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) along with their uncertainties. The radiative transfer solution is approximated by a two-stream approach, which allows the online calculation of the radiative transfer. Further processing of the visible bands includes the TAC (TIMELINE Atmospheric Correction) step, in which atmospheric correction is done on the basis of LUTs. BRDF is corrected using a temporal moving window approach. The thermal bands of the L1b product are used for LST estimation with SurfTemp. Its development included the selection of statistical procedures suitable for time series processing. In a round robin approach, the selected algorithms are tested on the basis of a large number of TOA radiance/LST pairs, which were generated using a radiative transfer model and the SeeBorV5 profile database. The algorithm comparison includes measures of precision, as well as the sensitivity of a method to the accuracy of its input data. Besides the implementation into SurfTemp, first cross-validation results between the AVHRR LST and MODIS LST are shown.

Optical properties (extinction efficiency, single scattering albedo, asymmetry parameter and scattering phase function) of five different desert dust minerals have been calculated with an asymptotic approximation approach (AAA) for non-spherical particles. The AAA method combines Rayleigh-limit approximations with an asymptotic geometric optics solution in a simple and straightforward formulation. The simulated extinction spectra have been compared with classical Lorenz–Mie calculations as well as with laboratory measurements of dust extinction. This comparison has been done for single minerals and with bulk dust samples collected from desert environments. It is shown that the non-spherical asymptotic approximation improves the spectral extinction pattern, including position of the extinction peaks, compared to the Lorenz–Mie calculations for spherical particles. Squared correlation coefficients from the asymptotic approach range from 0.84 to 0.96 for the mineral components whereas the corresponding numbers for Lorenz–Mie simulations range from 0.54 to 0.85. Moreover the blue shift typically found in Lorenz–Mie results is not present in the AAA simulations. The comparison of spectra simulated with the AAA for different shape assumptions suggests that the differences mainly stem from the assumption of the particle shape and not from the formulation of the method itself. It has been shown that the choice of particle shape strongly impacts the quality of the simulations. Additionally, the comparison of simulated extinction spectra with bulk dust measurements indicates that within airborne dust the composition may be inhomogeneous over the range of dust particle sizes, making the calculation of reliable radiative properties of desert dust even more complex.

Desert dust is characterized by strong silicate absorption bands located within the atmospheric window region in the terrestrial infrared (TIR) between 8 µm and 12 µm. These absorption bands and the corresponding optical properties (extinction efficiency, single scattering albedo, scattering phase function) have very specific spectral shapes for different silicate minerals, modulated by the particle size and shape. The asphericity of desert dust particles strongly affects the absorption band characteristics, for example due to surface wave modes for small particles. The use of the correct particle shape model significantly increases the spectral correlation between simulated dust optical properties for typical minerals and corresponding laboratory measurements for single minerals as well as for bulk dust from desert samples. The presence of absorption peaks and the spectral shape of the extinction signal carry dust information, which can be exploited for remote sensing purposes. With hyperspectral infrared methods it is thus possible to infer information beyond dust optical depth, that is to acquire information about dust particle size, composition and also vertical information. Examples of such information are shown for the Infrared Mineral Aerosol Retrieval Scheme (IMARS) which has been developed for the Infrared Atmospheric Sounding Interferometer (IASI) on board the European Metop satellite series. Another strong advantage of the hyperspectral signal from satellite instruments is the capability to minimize the influence of disturbing gas absorption lines within these bands. The probabilistic IMARS approach also directly provides the number of independent signals (variables) for each observation. For desert dust this number typically ranges from 2.5 to 4.0 depending on the characteristics of the observed dust plume. Consequently a lot more information beyond Aerosol Optical Depth (AOD) can be retrieved from these measurements.

Fields of dust aerosol optical depth (AOD) from numerical models and satellite observations are widely used data sets for evaluating the actual distribution of atmospheric dust aerosol. In this study we investigate the use of estimates of spatial and temporal correlation lengths (CLs) calculated from simulations using the regional model system COSMO-MUSCAT (COSMO: Consortium for Small-scale Modelling; MUSCAT: MUltiScale Chemistry Aerosol Transport Model) to characterize the spatial and temporal variability of atmospheric aerosol distribution, here mineral dust, and to provide an estimate on the temporal model output interval required in order to represent the local evolution of atmospheric dustiness. The CLs indicate the scales of variability for dust and thus provide an estimate for the stationarity of dust conditions in space and time. Additionally, CLs can be an estimate for the required resolution in time and space of observational systems to observe changes in atmospheric dust conditions that would be relevant for dust forecasts. Here, two years of dust simulations using COSMO-MUSCAT are analyzed. CLs for the individual years 2007 and 2008 are compared to the entire two-year period illustrating the impact of the length of time series on statistical analysis. The two years are chosen as they are contrasting with regard to mineral dust loads and thus provide additional information on the representativeness of the statistical analysis.Results from the COSMO-MUSCAT CL analysis are compared against CL estimates from satellite observations, here dust AOD inferred from IASI (Infrared Atmospheric Sounding Interferometer), which provides bi-daily information of atmospheric dust loading over desert land and ocean. Although as estimated from the satellite observations are at a generally lower level of values, the results demonstrate the applicability of daily observations for assessing the atmospheric dust distribution.Main outcomes of this study illustrate the applicability of CL for characterizing the spatio-temporal variability in atmospheric dustiness. This is in particular of interest for determining time intervals at which for example dust forecasts need to be provided. Results from this study further demonstrate that bi-daily satellite dust observations are sufficient for assessing the dust distribution over regions such as the Mediterranean region that are far from the dust sources. (C) 2015 Elsevier Ltd. All rights reserved.